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Keyword: forecast post-processing ×Clear all

4 claims from 2 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Deep learning for post-processing ensemble weather forecasts

   Grönquist, Yao, Ben‐Nun et al. · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2021

   The authors propose using fewer ensemble weather simulations plus deep neural network post-processing, and report better forecast skill, especially for extreme events, and comparable results to the full ensemble.

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   1. UncheckedApplied to global data, the authors' mixed models improve ensemble weather forecast skill, measured by CRPS, by more than 14% in relative terms.“Applied to global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%.”
   2. UncheckedThe paper reports that its deep-learning post-processing improves forecasts more for extreme weather events, in selected case studies.“Furthermore, we demonstrate that the improvement is larger for extreme weather events on select case studies.”
   3. UncheckedThe authors say their deep-learning post-processing can reach results comparable to a full forecast ensemble while using fewer simulated trajectories.“We also show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100‐m scale weather and climate models

   Lean, Theeuwes, Baldauf et al. · Quarterly Journal of the Royal Meteorological Society · 2024

   The article reviews the gaps between research on 100-m scale weather models and practical operational use, and suggests directions for future work on cost, dynamics, physics, observations, data assimilation and ensembles.

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   1. UncheckedAt 100-m grid scales, some old modelling problems such as convection get easier, but new ones, such as surface parameterisation, appear.“There are a number of challenges around model parameterisations, where some of the traditional problems (e.g., convection) become easier but a number of new challenges (e.g., around surface parameterisations) appear.”